Lecture 16 Gene expression and the transcriptome I

Genomics and transcriptome After genome sequencing and annotation, the second major branch of genomics is analysis of the transcriptome The transcriptome is the complete set of transcripts and their relative levels of expression in particular cells or tissues under defined conditions

Thesis: the analysis of gene expression data is going to be big in 21st century statistics Many different technologies, including High-density nylon membrane arrays Serial analysis of gene expression (SAGE) Short oligonucleotide arrays (Affymetrix) Long oligo arrays (Agilent) Fibre optic arrays (Illumina) cDNA arrays (Brown/Botstein)*

Total microarray articles indexed in Medline 1995 1996 1997 1998 1999 2000 2001 100 200 300 400 500 600 (projected) Year Number of papers

Common themes Parallel approach to collection of very large amounts of data (by biological standards) Sophisticated instrumentation, requires some understanding Systematic features of the data are at least as important as the random ones Often more like industrial process than single investigator lab research Integration of many data types: clinical, genetic, molecular…..databases

Biological background G U A A U C C RNA polymerase mRNA Transcription DNA G T A A T C C T C | | | | | | | | | C A T T A G G A G

Idea: measure the amount of mRNA to see which genes are being expressed in (used by) the cell. Measuring protein directly might be better, but is currently harder.

Clone cDNA strands, complementary to the mRNA Reverse transcription Clone cDNA strands, complementary to the mRNA mRNA G U A A U C C U C Reverse transcriptase cDNA T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G

Transcriptome datasets cDNA microarrays Oligonucleotide arrays Most suitable for contrasting expression levels across tissues and treatments of chosen subset of genome Serial analysis of gene expression (SAGE) Relies on counting sequence tags to estimate absolute transcript levels, but less suited to replication

What is a microarray Slide or membrane with numerous probes that represent various genes of some biological species. Probes are either oligo-nucleotides that range in length from 25 to 60 bases, or cDNA clones with length from a hundred to several thousand bases. The array type corresponds to a list of reference genes on the microarray with annotations. For example: (1) 22K Agilent oligo array, and (2) NIA 15K cDNA membrane array. Man individual users want to add their own array types to the list.

What happens to a microarray Microarrays are hybridized with labeled cDNA synthesized from a mRNA-sample of some tissue. The intensity of label (radioactive or fluorescent) of each spot on a microarray indicates the expression of each gene. One-dye arrays (usually with radioactive label) show the absolute expression level of each gene. Two-dye arrays (fluorescent label only) can indicate relative expression level of the same gene in two samples that are labelled with different colours and mixed before hybridization. One of these samples can be a universal reference which helps to compare samples that were hybridized on different arrays.

Universal reference Universal reference is a mixture of cDNA that represents (almost) all genes of a species, while their relative abundance is standardized. Universal reference is synthesized from mRNA of various tissues. Universal reference can be used as a second sample for hybridization on 2-dye microarrays. Then all other samples become comparable via the universal reference.

cDNA microarrays cDNA clones

cDNA microarrays Compare the genetic expression in two samples of cells PRINT cDNA from one gene on each spot SAMPLES cDNA labelled red/green with fluorescent dyes e.g. treatment / control normal / tumor tissue Robotic printing

Add equal amounts of labelled cDNA samples to microarray. SCAN HYBRIDIZE Add equal amounts of labelled cDNA samples to microarray. SCAN Laser Detector Detector measures ratio of induced fluorescence of two samples Sample is spread evenly over microarray, specific cDNAs then hybridize with their counterparts on the array, after which the sample is rinsed off to only leave hybridized sample

16-bit TIFF files (Rfg, Rbg), (Gfg, Gbg) R, G Biological question Differentially expressed genes Sample class prediction etc. Experimental design Microarray experiment 16-bit TIFF files Image analysis (Rfg, Rbg), (Gfg, Gbg) Normalization R, G Estimation Testing Clustering Discrimination Biological verification and interpretation

cDNA microarray experiments mRNA levels compared in many different contexts Different tissues, same organism (brain versus liver) Same tissue, same organism (treatment v. control, tumor v. non-tumor) Same tissue, different organisms (wildtype v. knock-out, transgenic, or mutant) Time course experiments (effect of treatment, development) Other special designs (e.g. to detect spatial patterns).

Replication An independent repeat of an experiment. In practice it is impossible to achieve absolute independence of replicates. For example, the same researcher often does all the replicates, but the results may differ in the hands of another person. But it is very important to reduce dependency between replicates to a minimum. For example, it is much better to take replicate samples from different animals (these are called biological replicates) than from the same animal (these would be technical replicates), unless you are interested in a particular animal. If sample preparation requires multiple steps, it is best if samples are separated from the very beginning, rather than from some intermediate step. Each replication may have several subreplications (=technical replications).

Some statistical questions Image analysis: addressing, segmenting, quantifying Normalisation: within and between slides Quality: of images, of spots, of (log) ratios Which genes are (relatively) up/down regulated? Assigning p-values to tests/confidence to results.

Some statistical questions, ctd Planning of experiments: design, sample size Discrimination and allocation of samples Clustering, classification: of samples, of genes Selection of genes relevant to any given analysis Analysis of time course, factorial and other special experiments…..…...& much more.

Some bioinformatic questions Connecting spots to databases, e.g. to sequence, structure, and pathway databases Discovering short sequences regulating sets of genes: direct and inverse methods Relating expression profiles to structure and function, e.g. protein localisation Identifying novel biochemical or signalling pathways, ………..and much more.

Some basic problems…. …with automatically scanning the microarrays

What types of things can go wrong? Spot size variances Dye labeling efficiency differences (performing dye swap experiments and/or improving dye labeling protocols help) Positional biases (can be due to print tip, spot drying time dependencies, hybridizations not being uniform, etc.) Plate biases Variance in background (dye sticking to the array, dust, hairs, defects in the array coating, etc.) Scanner non-linearities Sample biases (Ex: contamination of DNA in your RNA sample, sample handling, storage, and preparation protocol variances)

Part of the image of one channel false-coloured on a white (v Part of the image of one channel false-coloured on a white (v. high) red (high) through yellow and green (medium) to blue (low) and black scale

Does one size fit all?

Segmentation: limitation of the fixed circle method Segmented regions Fixed Circle Inside the boundary is spot (foreground), outside is not – Background pixels are those immediately surrounding circle/segment boundary

Quantification of expression For each spot on the slide we calculate Red intensity = Rfg - Rbg fg = foreground, bg = background, and Green intensity = Gfg - Gbg and combine them in the log (base 2) ratio Log2( Red intensity / Green intensity)

Gene Expression Data = slide 1 slide 2 slide 3 slide 4 slide 5 … On p genes for n slides: p is O(10,000), n is O(10-100), but growing Slides slide 1 slide 2 slide 3 slide 4 slide 5 … 1 0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49 0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10 0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.06 1.06 1.35 1.09 -1.09 ... Genes Gene expression level of gene 5 in slide 4 = Log2( Red intensity / Green intensity) These values are conventionally displayed on a red (>0) yellow (0) green (<0) scale.

The red/green ratios can be spatially biased . Top 2.5%of ratios red, bottom 2.5% of ratios green

The red/green ratios can be intensity-biased if one dye is under-incorporated relative to the other M = log2R/G = log2R - log2G Values should scatter about zero. A = log2(RG)/2 = (log2R + log2G)/2

Normalization: how we “fix” the previous problem Loess transformation (Yang et al., 2001) The curved line becomes the new zero line Orange: Schadt-Wong rank invariant set Red line: Loess smooth

Normalizing: before -4

Normalizing: after

Normalisation of microarray data Red Green Diff R(G/R) Log2R Norm. 16500 15104 -1396 0.915 -0.128 -0.048 357 158 -199 0.443 -1.175 -1.095 8250 8025 -225 0.973 -0.039 0.040 978 836 -142 0.855 -0.226 -0.146 65 89 24 1.369 0.453 0.533 684 1368 539 2.000 1.000 1.080 13772 11209 -2563 0.814 -0.297 -0.217 856 731 -125 0.854 -0.228 -0.148

Analysis of Variance (ANOVA) approach ANOVA is a robust statistical procedure Partitions sources of variation, e.g. whether variation in gene expression is less in subset of data than in total data set Requires moderate levels of replication (4-10 replicates of each treatment) But no reference sample needed Expression judged according to statistical significance instead of by adopting arbitrary thresholds

Contributions to measured gene expression level yijkg = μ + Ai + (VG)kg + (AG)ig + (DG)jg + εijkg Noise Dye effect Array effect Spot effect expression level Gene expresion level (y) of 'Gene A' All these noise effects (grey, blue) are taken into account to discern the best possible signal (yellow)

Analysis of Variance (ANOVA) approach has two steps Raw fluorescence data is log-transformed and arrays and dye channels are normalised with respect to one another. You get normalised expression levels where dye and array effects are eliminated A second model is fit to normalised expression levels associated with each individual gene

Analysis of Variance (ANOVA) approach Advantage: design does not need reference samples Concern: treatments should be randomised and all single differences between treatments should be covered E.g., if male kidney and female liver are contrasted on one set, and female kidney and male liver on another, we cannot state whether gender or tissue type is responsible for expression differences observed

Analysis of Variance (ANOVA) experimental microarray setups Loop design of experiments possible: A-B, B-C, C-D, and D-A Flipping of dyes (dye swap) to filter artifacts due to preferential labeling Repeating hybridization on two-dye microarrays with the same samples but swapped fluorescent labels. For example, sample A is labeled with Cy3 (green) and sample B with Cy5 (red) in the first array, but sample A is labeled with Cy5 and sample B with Cy3 in the second array. Dye swap is used to remove technical colour bias in some genes. Dye swap is a technical replication (=subreplication). Completely or partially randomised designs

“Experimental Design for Gene Expression Microarrays” Kerr, et. al. Biostatistics, 2, 183-201 (2000) “Experimental Design for Gene Expression Microarrays” Loop Design Can detect gene specific dye effccts!!! All varieties are evenly sampled (better for the statistics)!!! You don’t waste resources sampling the reference sample (which is not of ultimate interest to you) so many times!!! But you need to label each sample with both Green and Red dyes. …and across loop comparisons lose information in large loops Reference Design Typical Microarray Design Can not detect gene specific dye effects!!! Augmented Reference At least you get some gene specific dye effects (even though you don’t get array/array-gene specific dye effects) Equations get nasty with dyes and varieties being partially confounded. Modified Loop Design Even distribution of varieties without having to label each sample with 2 dyes Can not detect gene specific dye effccts!!!

Analysis of Variance (ANOVA) Within-array variance among replicated clones is much lower than between-array variance, due to stoichiometry of labeling during reverse transcription So do not duplicate spots on same array, this renders effects seemingly large

Oligonucleotide arrays Affymetrix GeneChip No cDNA library but 25-mer oligonucleotides Oligomers designed by computer program to represent known or predicted open reading frames (ORFs)

Oligonucleotide arrays Up to 25 oligos designed for each exon Each oligo printed on chip adjacent to (single base pair) mismatch oligo Match/mismatch oligos used to calculate signal intensity and then expression level But: not everybody agrees with Affymetrix mismatch strategy: is it biologically relevant?

Oligonucleotide arrays High-density oligonucleotide chips are constructed on a silicon chip by photolithography and combinatorial chemistry Several hundred thousand oligos with mismatch control can be rapidly synthesised on thousands of identical chips Expensive technology – individual chips cost hundreds of Dollars Cost is issue with degree of replication

Some statistical research stimulated by microarray data analysis Experimental design : Churchill & Kerr Image analysis: Zuzan & West, …. Data visualization: Carr et al Estimation: Ideker et al, …. Multiple testing: Westfall & Young , Storey, …. Discriminant analysis: Golub et al,… Clustering: Hastie & Tibshirani, Van der Laan, Fridlyand & Dudoit, …. Empirical Bayes: Efron et al, Newton et al,…. Multiplicative models: Li &Wong Multivariate analysis: Alter et al Genetic networks: D’Haeseleer et al and more

Comparing gene expression profiles

Breast tumor classification Example 1 Breast tumor classification van 't Veer et al (2002) Nature 415, 530 Dutch Cancer Institute (NKI) Prediction of clinical outcome of breast cancer DNA microarray experiment 117 patients 25000 genes

Validation set: 2 out of 19 incorrect 78 sporadic breast tumors 70 prognostic markers genes Good prognosis Bad prognosis

Is there work to do? Example 1 What is the minimum number of genes required in these classification models (to avoid chance classification) What is the maximum number of genes (avoid overfitting) What is the relation to the number of samples that must be measured? Rule of thumb: minimal number of events per variable (EPV)>10 NKI study ~35 tumors (events) in each group  35/10=3.5 genes should maximally have been selected (70 were selected in the breast cancer study)  overfitting? Is the classification model adequate?

Example 2 Apo AI experiment (Callow et al 2000, LBNL) Goal. To identify genes with altered expression in the livers of Apo AI knock-out mice (T) compared to inbred C57Bl/6 control mice (C). Apo-lipoproteins are involved in lipid transport. 8 treatment mice and 8 control mice 16 hybridizations: liver mRNA from each of the 16 mice (Ti , Ci ) is labelled with Cy5, while pooled liver mRNA from the control mice (C*) is labelled with Cy3. Probes: ~ 6,000 cDNAs (genes), including 200 related to lipid metabolism.

Example 3 Leukemia experiments (Golub et al 1999,WI) Goal. To identify genes which are differentially expressed in acute lymphoblastic leukemia (ALL) tumours in comparison with acute myeloid leukemia (AML) tumours. 38 tumour samples: 27 ALL, 11 AML. Data from Affymetrix chips, some pre-processing. Originally 6,817 genes; 3,051 after reduction. Data therefore a 3,051  38 array of expression values.